Secure and Resilient Machine Learning System for Detecting Fifth Generation (5G) Attacks including Zero-Day Attacks

This project will implement an advanced machine learning based 5G attack detection system that can achieve high classification speed (10k packets per second) with high accuracy (90% or greater) as well as address a vulnerability to zero-day attacks (90% accuracy against real zero-day attacks recorded by Amazon Web Services) using field programmable gate array based deep autoencoders.